{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T02:40:53Z","timestamp":1781145653010,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":34,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819981441","type":"print"},{"value":"9789819981458","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8145-8_16","type":"book-chapter","created":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T23:02:21Z","timestamp":1701039741000},"page":"191-206","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Phishing Scam Detection for\u00a0Ethereum Based on\u00a0Community Enhanced Graph Convolutional Networks"],"prefix":"10.1007","author":[{"given":"Keting","family":"Yin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4409-4366","authenticated-orcid":false,"given":"Binglong","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"key":"16_CR1","unstructured":"Abu-El-Haija, S., Kapoor, A., Perozzi, B., Lee, J.: N-GCN: multi-scale graph convolution for semi-supervised node classification. In: Uncertainty in Artificial Intelligence, pp. 841\u2013851. PMLR (2020)"},{"issue":"1","key":"16_CR2","doi-asserted-by":"publisher","first-page":"10692","DOI":"10.1038\/s41598-019-47119-2","volume":"9","author":"M Bellingeri","year":"2019","unstructured":"Bellingeri, M., Bevacqua, D., Scotognella, F., Cassi, D.: The heterogeneity in link weights may decrease the robustness of real-world complex weighted networks. Sci. Rep. 9(1), 10692 (2019)","journal-title":"Sci. Rep."},{"issue":"4\u20135","key":"16_CR3","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.physrep.2005.10.009","volume":"424","author":"S Boccaletti","year":"2006","unstructured":"Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.U.: Complex networks: structure and dynamics. Phys. Rep. 424(4\u20135), 175\u2013308 (2006)","journal-title":"Phys. Rep."},{"key":"16_CR4","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45, 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Cao, S., Lu, W., Xu, Q.: GraRep: learning graph representations with global structural information. In: Proceedings of the 24th ACM International on Conference on Information And Knowledge Management, pp. 891\u2013900 (2015)","DOI":"10.1145\/2806416.2806512"},{"issue":"3","key":"16_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1961189.1961199","volume":"2","author":"CC Chang","year":"2011","unstructured":"Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 1\u201327 (2011)","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Chen, J., Zhang, J., Chen, Z., Du, M., Xuan, Q.: Time-aware gradient attack on dynamic network link prediction. IEEE Transactions on Knowledge and Data Engineering (2021)","DOI":"10.1109\/TKDE.2021.3110580"},{"issue":"1","key":"16_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3398071","volume":"21","author":"L Chen","year":"2020","unstructured":"Chen, L., Peng, J., Liu, Y., Li, J., Xie, F., Zheng, Z.: Phishing scams detection in Ethereum transaction network. ACM Trans. Internet Technol. (TOIT) 21(1), 1\u201316 (2020)","journal-title":"ACM Trans. Internet Technol. (TOIT)"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794 (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Chen, W., Zhang, T., Chen, Z., Zheng, Z., Lu, Y.: Traveling the token world: a graph analysis of Ethereum ERC20 token ecosystem. In: Proceedings of The Web Conference 2020, pp. 1411\u20131421 (2020)","DOI":"10.1145\/3366423.3380215"},{"key":"16_CR11","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855\u2013864 (2016)","DOI":"10.1145\/2939672.2939754"},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"Holub, A., O\u2019Connor, J.: Coinhoarder: Tracking a Ukrainian bitcoin phishing ring DNS style. In: 2018 APWG Symposium on Electronic Crime Research (eCrime), pp. 1\u20135. IEEE (2018)","DOI":"10.1109\/ECRIME.2018.8376207"},{"key":"16_CR14","unstructured":"Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"16_CR15","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"16_CR16","unstructured":"Li, S., Xu, F., Wang, R., Zhong, S.: Self-supervised incremental deep graph learning for Ethereum phishing scam detection. arXiv preprint arXiv:2106.10176 (2021)"},{"key":"16_CR17","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45370-9","volume-title":"E-commerce Agents: Marketplace Solutions, Security Issues, and Supply And Demand","author":"J Liu","year":"2001","unstructured":"Liu, J.: E-commerce Agents: Marketplace Solutions, Security Issues, and Supply And Demand, vol. 2033, 1st edn. Springer Science & Business Media, Heidelberg (2001). https:\/\/doi.org\/10.1007\/3-540-45370-9","edition":"1"},{"key":"16_CR18","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1016\/j.patrec.2020.08.015","volume":"138","author":"Y Liu","year":"2020","unstructured":"Liu, Y., Wang, Q., Wang, X., Zhang, F., Geng, L., Wu, J., Xiao, Z.: Community enhanced graph convolutional networks. Pattern Recogn. Lett. 138, 462\u2013468 (2020)","journal-title":"Pattern Recogn. Lett."},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Liu, Z., Chen, C., Yang, X., Zhou, J., Li, X., Song, L.: Heterogeneous graph neural networks for malicious account detection. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 2077\u20132085 (2018)","DOI":"10.1145\/3269206.3272010"},{"key":"16_CR20","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)"},{"key":"16_CR21","unstructured":"Narayanan, A., Chandramohan, M., Venkatesan, R., Chen, L., Liu, Y., Jaiswal, S.: graph2vec: Learning distributed representations of graphs. arXiv preprint arXiv:1707.05005 (2017)"},{"issue":"23","key":"16_CR22","doi-asserted-by":"publisher","first-page":"8577","DOI":"10.1073\/pnas.0601602103","volume":"103","author":"ME Newman","year":"2006","unstructured":"Newman, M.E.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577\u20138582 (2006)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"16_CR23","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701\u2013710 (2014)","DOI":"10.1145\/2623330.2623732"},{"key":"16_CR24","doi-asserted-by":"crossref","unstructured":"Pilkington, M.: Blockchain technology: principles and applications. In: Research Handbook on Digital Transformations, pp. 225\u2013253. Edward Elgar Publishing (2016)","DOI":"10.4337\/9781784717766.00019"},{"key":"16_CR25","doi-asserted-by":"crossref","unstructured":"Qiu, J., Tang, J., Ma, H., Dong, Y., Wang, K., Tang, J.: DeepInf: social influence prediction with deep learning. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2110\u20132119 (2018)","DOI":"10.1145\/3219819.3220077"},{"key":"16_CR26","unstructured":"Rish, I., et al.: An empirical study of the Naive Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, vol. 3, pp. 41\u201346 (2001)"},{"key":"16_CR27","doi-asserted-by":"crossref","unstructured":"Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067\u20131077 (2015)","DOI":"10.1145\/2736277.2741093"},{"key":"16_CR28","doi-asserted-by":"crossref","unstructured":"Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1225\u20131234 (2016)","DOI":"10.1145\/2939672.2939753"},{"key":"16_CR29","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/978-981-16-7993-3_15","volume-title":"Blockchain and Trustworthy Systems","author":"J Wang","year":"2021","unstructured":"Wang, J., Chen, P., Yu, S., Xuan, Q.: TSGN: transaction subgraph networks for identifying Ethereum phishing accounts. In: Dai, H.-N., Liu, X., Luo, D.X., Xiao, J., Chen, X. (eds.) BlockSys 2021. CCIS, vol. 1490, pp. 187\u2013200. Springer, Singapore (2021). https:\/\/doi.org\/10.1007\/978-981-16-7993-3_15"},{"issue":"11","key":"16_CR30","doi-asserted-by":"publisher","first-page":"2266","DOI":"10.1109\/TSMC.2019.2895123","volume":"49","author":"S Wang","year":"2019","unstructured":"Wang, S., Ouyang, L., Yuan, Y., Ni, X., Han, X., Wang, F.Y.: Blockchain-enabled smart contracts: architecture, applications, and future trends. IEEE Trans. Syst. Man Cybern. Syst. 49(11), 2266\u20132277 (2019)","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"issue":"6684","key":"16_CR31","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1038\/30918","volume":"393","author":"DJ Watts","year":"1998","unstructured":"Watts, D.J., Strogatz, S.H.: Collective dynamics of \u2018small-world\u2019 networks. Nature 393(6684), 440\u2013442 (1998)","journal-title":"Nature"},{"issue":"2","key":"16_CR32","doi-asserted-by":"publisher","first-page":"1156","DOI":"10.1109\/TSMC.2020.3016821","volume":"52","author":"J Wu","year":"2020","unstructured":"Wu, J., et al.: Who are the phishers? Phishing scam detection on Ethereum via network embedding. IEEE Trans. Syst. Man Cybern. Syst. 52(2), 1156\u20131166 (2020)","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"issue":"6","key":"16_CR33","doi-asserted-by":"publisher","first-page":"2776","DOI":"10.1109\/TKDE.2019.2957755","volume":"33","author":"Q Xuan","year":"2019","unstructured":"Xuan, Q., et al.: Subgraph networks with application to structural feature space expansion. IEEE Trans. Knowl. Data Eng. 33(6), 2776\u20132789 (2019)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"1","key":"16_CR34","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1109\/TBDATA.2018.2850013","volume":"6","author":"D Zhang","year":"2018","unstructured":"Zhang, D., Yin, J., Zhu, X., Zhang, C.: Network representation learning: a survey. IEEE Trans. Big Data 6(1), 3\u201328 (2018)","journal-title":"IEEE Trans. Big Data"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8145-8_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T18:55:29Z","timestamp":1710356129000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8145-8_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,27]]},"ISBN":["9789819981441","9789819981458"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8145-8_16","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,27]]},"assertion":[{"value":"27 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"650","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.14","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.46","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}